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This paper proposes a novel downlink spatial scheduling algorithm in multiuser multiple-input-multiple-output (MIMO) systems, which selects a good combination of terminals and base station's (BS's) transmit beams so that the BS's beams nullify interstream interference at the selected terminals. In the derivation process, we reveal the new property that the optimization problem of downlink spatial scheduling is equivalent to that of uplink scheduling under the BS's zero-forcing beamforming. Using this property, an efficient downlink scheduling algorithm is presented, applying the principle of an uplink scheduling algorithm. Numerical results show that the presented spatial scheduling achieves a much higher system throughput than a multiuser MIMO system without spatial scheduling or with conventional spatial scheduling by linear processing. We also present a realistic control structure to achieve the uplink and downlink spatial scheduling in time-division duplex systems. Index Terms--Beams, mobile communications, multiple-inputmultiple-output (MIMO) systems, scheduling, traffic control.

As networks expand in area and scale, there arise needs for high-speed, optimal path-design algorithms that can take into account a number of factors. This paper proposes an optimal path-design algorithm using a neural network. The proposed algorithm can be implemented by hardware digital logic circuits, and enables optimal path design to be performed 104 to 106 times faster than the conventional polynomial-time algorithms. The proposed method is also easily implemented in LSI. Another feature of the proposed method is the use of directed and weighted synaptic connections. © 1998 Scripta Technica, Electron Comm Jpn Pt 1, 81(11): 67–76, 1998 [ABSTRACT FROM AUTHOR]

Recently, application of neural networks using their parallel processing feature has progressed. This paper proposes a routing method using a neural network and based on dynamic programming. The proposed method finds the least-hop path between two points in a communication network. The method consists of the application of disturbance to the destination, propagation of the disturbance to the source, and search for the shortest path from the source to the destination, using the distribution of the influences of the disturbance at each node. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 1, 80(8): 1–6, 1997 [ABSTRACT FROM AUTHOR]